Physiological Load Estimation from Location Data
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Solution Overview
Problem
Conventional RFID-based systems fail to accurately determine the overall physiological load experienced by athletes or objects, as they only consider directional acceleration and do not account for momentum, leading to imprecise and incomplete analysis of physical loads during movements.
Innovation Solution
The system collects location and acceleration data to calculate both directional and lateral load vectors, incorporating momentum into the analysis, and scales the load vectors for clear and insightful representation, using methods like Kalman filters and Fourier transforms to refine the data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional RFID-based systems only consider directional acceleration, then the system complexity remains low, but the measurement precision of physiological load is insufficient
Solution Approach 1:
The load vector calculation is segmented into two independent components: directional load vector (based on directional acceleration) and lateral load vector (based on lateral acceleration). This segmentation allows the system to comprehensively capture physiological load from multiple directions while maintaining modular calculation that does not significantly increase system complexity.
Solution Approach 2:
The system transitions from one-dimensional directional acceleration analysis to two-dimensional analysis by incorporating lateral acceleration. This dimensional expansion enables comprehensive physiological load measurement by considering both directional and lateral components, thereby improving measurement precision without requiring complex three-dimensional tracking systems.
2Loss of information
If conventional systems do not account for momentum, then the calculation process remains simple, but the analysis completeness of physiological load is insufficient
Solution Approach 1:
The system performs preliminary filtering of location observations using Kalman filters before calculating acceleration and momentum. This preliminary action removes noise and anomalies from raw data, ensuring that subsequent momentum calculations are based on clean, reliable data, thereby improving analysis completeness without adding complex real-time processing requirements.
Solution Approach 2:
The system incorporates feedback mechanisms by using Kalman filters that continuously refine location estimates based on previous states and current measurements. This feedback loop ensures that momentum calculations are based on accurate, continuously updated position data, improving the completeness of physiological load analysis while maintaining efficient calculation processes.
3Measurement precision
If the system collects and processes multiple data parameters (location, velocity, acceleration, jerk), then the measurement precision improves, but the loss of time for data processing increases
Solution Approach 1:
The system continuously tracks and processes location observations over time, maintaining a continuous stream of data for location, velocity, acceleration, and jerk. This continuous action allows the system to compute physiological load parameters in real-time or near-real-time, minimizing processing delays while ensuring high measurement precision through comprehensive data collection.
Solution Approach 2:
The system performs preliminary filtering and smoothing of raw location data using Kalman filters and other signal processing techniques before deriving velocity, acceleration, and jerk parameters. This preliminary action reduces noise and anomalies in the data, enabling more efficient and accurate computation of physiological load parameters without requiring excessive processing time.
4Measurement precision
If the system uses filtering methods like Kalman filter and Fourier transform, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The system replaces complex mechanical or hardware-based filtering mechanisms with software-based algorithms such as Kalman filters and Fourier transforms. This substitution allows the implementation of sophisticated signal processing and noise reduction techniques without requiring additional physical components, thereby improving location data accuracy while avoiding significant increases in device complexity.
Data Source
AI summary
Methods and devices for determining a load vector on an object are disclosed herein. An example method includes collecting location observations related to the object. The example method further includes filtering the location observations to determine an estimated model path. The example method further includes outputting a set of data from the estimated model path, wherein the set of data includes a model location, a model velocity, a model acceleration, and a model jerk. The example method further includes calculating a load vector from the set of data, scaling the load vector via a scaling index, and transmitting the scaled load vector to a remote device.